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This chapter seeks to present a limited overview of some aspects of manipulated and/or fake images that contribute to society ‘becoming post-truth’. It subclassifies levels of manipulation and also presents the finding from a descriptive survey that gauges perceptions on awareness and recognisability of fake images. It also presents perceptions of effect on individuals of images modified for aesthetic reasons and carried by social media. The majority of respondents seemed affected by this, but with only a minority whose perception of self was affected. Another result of the survey is that there is a general mistrust of images not carried by gatekept sources.

With trade amounting to more than US$400bn, counterfeiting is already affecting many successful brands. Often, consumers are deceived into buying fake products due to the visual similarity between fake and original brand logos. This paper aims to explore the varying forms of fraudulent imitation of original brand logotypes (operationalized at the level of logotype transposition), which can aid in the detection of a counterfeit brand.

Across two studies, this research tested how well consumers can differentiate counterfeit from original logos of well-known brands both explicitly and implicitly. Seven popular brand logos were altered to create different levels of visual dissimilarity and participants were required to discriminate the logos as fake or genuine.

Results demonstrate that although consumers can explicitly discriminate fake logos with a high degree of accuracy, the same is not true under conditions in which logos are presented very briefly (tapping participants’ implicit or automatic logo recognition capabilities), except when the first and last letters of the logotype are substituted.

A large body of research on counterfeit trade focuses on the individual or cross-cultural differences behind the prevalence of counterfeit trade. There is limited research exploring the ability of a consumer to correctly identify a fake logo, based on its varying similarity with the original logotype; this paper addresses this gap. Given that many of the purchase decisions are often made automatically, identifying key implicit differentiators that can help a consumer recognize a fake logo should be informative to both practitioners and academics.

This paper presents recently published resources on library instruction and information literacy. It provides an introductory overview and a selected annotated bibliography of publications covering various library types, study populations and research contexts. The selected bibliography is useful to efficiently keep up with trends in library instruction for busy practitioners, library science students and those wishing to learn about information literacy in other contexts.

This article annotates 424 English-language periodical articles, monographs, dissertations, theses and reports on library instruction and information literacy published in 2021. The sources were selected from the EBSCO platform for Library, Information Science, and Technology Abstracts (LISTA), Education Resources Information Center (ERIC), Scopus, ProQuest Dissertations and Theses, and WorldCat, published in 2021 that included the terms “information literacy,” “library instruction,” or “information fluency” in the title, abstract or keywords. The sources were organized in Zotero. Annotations summarize the source, focusing on the findings or implications. Each source was categorized into one of seven pre-determined categories: K-12 Education, Children and Adolescents; Academic and Professional Programs; Everyday Life, Community, and the Workplace; Libraries and Health Information Literacy; Multiple Library Types; and Other Information Literacy Research and Theory.

The paper provides a brief description of 424 sources and highlights sources that contain unique or significant scholarly contributions.

The information may be used by librarians, researchers and anyone interested as a quick and comprehensive reference to literature on library instruction and information literacy within 2021.

The purpose of this paper is to examine the factors that significantly affect the prediction of fake news from the virality theory perspective. The paper looks at a mix of emotion-driven content, sentimental resonance, topic modeling and linguistic features of news articles to predict the probability of fake news.

A data set of over 12,000 articles was chosen to develop a model for fake news detection. Machine learning algorithms and natural language processing techniques were used to handle big data with efficiency. Lexicon-based emotion analysis provided eight kinds of emotions used in the article text. The cluster of topics was extracted using topic modeling (five topics), while sentiment analysis provided the resonance between the title and the text. Linguistic features were added to the coding outcomes to develop a logistic regression predictive model for testing the significant variables. Other machine learning algorithms were also executed and compared.

The results revealed that positive emotions in a text lower the probability of news being fake. It was also found that sensational content like illegal activities and crime-related content were associated with fake news. The news title and the text exhibiting similar sentiments were found to be having lower chances of being fake. News titles with more words and content with fewer words were found to impact fake news detection significantly.

Several systems and social media platforms today are trying to implement fake news detection methods to filter the content. This research provides exciting parameters from a viral theory perspective that could help develop automated fake news detectors.

While several studies have explored fake news detection, this study uses a new perspective on viral theory. It also introduces new parameters like sentimental resonance that could help predict fake news. This study deals with an extensive data set and uses advanced natural language processing to automate the coding techniques in developing the prediction model.

This paper aims to examine how content relevancy influences consumers’ preference to receive and share fake news. Further, it investigates how these receivers perceive the social image of the people who share fake news. Finally, this study examines how brand strength and valence and credibility of fake content influence consumer’s word-of-mouth recommendations, purchase intentions and attitude toward the brand.

Three experiments were conducted to test the hypotheses. The data was analyzed using a two-way analysis of variance and PROCESS techniques.

Findings indicate that people prefer to receive and share relevant content, even if it is fake. Sharing fake news conveys the sender’s sociability but also creates a negative perception of narcissism. Individuals are more likely to recommend a brand if the fake news is perceived as credible and positive (vs negative). Finally, brand-strength can help brands to negate the harmful effects of fake news.

Future research can explore the role of group dynamics, tie-strength and media richness (text, image and videos) in the dispersion of fake news and its impact on brands.

Marketers should communicate and educate consumers that sharing fake content can harm their social image, which can reduce information dispersion. Marketers should also improve brand-strength that can protect the brand against the adverse impact of fake news.

This study contributes to the emerging literature on fake news by studying the impact of fake news on consumer intentions and attitudes toward the brand, which are critical for the success of any brand.

By reviewing different information visualization techniques for securing web information systems, this paper aims to provide a foundation for further studies of the same topic. Another purpose of the paper is to discover directions in which there is a lack of extensive research, thereby encouraging more investigations.

The related techniques are classified first by their locations in the web information systems architecture: client side, server side, and application side. Then the techniques in each category are further classified based on attributes specific to that category.

Although there is much research on information visualization for securing web browser user interface and server side systems, there are very few studies about the same techniques on web application side.

This paper is the first published paper reviewing extensively information visualization techniques for securing web information systems. The classification used here offers a framework for further studies as well as in‐depth investigations.

This paper aims to explain how consumer persuasion knowledge and perceived deception in advertisements can influence consumers’ future evaluation of fake news about a brand.

This research develops a conceptual model using widely used persuasion knowledge theory and confirmation bias theory. A questionnaire-based online survey (n = 410) was conducted by displaying an advertisement stimulus followed by a fake news stimulus to test the model. Covariance-based structural equation modeling was used to analyze the hypothesized research model.

The results demonstrate that consumers with high persuasion knowledge are more likely to trust and adopt fake news about an advertised brand through the mediation of perceived deception in the advertisement. Additionally, perceived deception indirectly affects information adoption through the mediation of news credibility.

Theoretically, this study contributes to the existing body of literature on advertising deception and fake news. This research also extends theory of persuasion knowledge in understanding adoption of fake news. Practically, this study has significant implications for various stakeholders, including brands, social media corporations and consumers.

This research adds novel insights in the relationship of consumers’ persuasion knowledge and credibility and adoption of fake news. Furthermore, the investigation of the relationship between the perceived deception in advertising and the adoption of fake news has not been explored, which is also novel.

The emergence of the novel coronavirus (COVID-19) has threatened physical and mental health, and changed the behaviour and decision-making processes of individuals, organisations, and institutions worldwide. As many services move online due to the pandemic, COVID-19-themed cyber fraud is also growing. This article explores cyber fraud victimization and cyber security threats during COVID-19 using psychological and traditional criminological theories. It also provides a COVID-19-themed cyber fraud typology using empirical evidence from institutional and agency reports. Through organizing COVID-19-themed cyber fraud into four different categorizations, we aim to offer classification insights to researchers and industry professionals so that stakeholders can effectively manage emerging cyber fraud risks in our current pandemic.

The approach the study take for this conceptual paper is typology.